---
title: "Gorilla Home Range"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
    orientation: columns
    vertical_layout: fill
---
     

```{r setup, include=FALSE}
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

library(flexdashboard)
library(DT)
library(ggplot2)
library(plotly)
```

# Main Page

## Column {data-width=300}

###


This dashboard is an analysis tool to compute and visualize home range data of Mountain Gorillas collected by GNSS sensors from [Dian Fossey Gorilla Fund International](https://gorillafund.org/). The observed Gorillas are located at the Volcanoes National Park, Rwanda. This analysis tool enables: 




  
Compute home range sizes, walking distances, and overlap areas
  
Export statistical data tables and spatial results
  
Data visualization of home range analysis




License: https://creativecommons.org/licenses/by-nc/3.0/

Author: [Ka Hei Chow](http://students.eagle-science.org/students/students-2020/ka-hei/)








![](https://www.gorillasafaricompany.com/wp-content/uploads/2019/03/logo-dian-fossey.png){width=70%}


## Column {data-width=700}

###

```{r picture, echo = F, out.width = '100%'}
knitr::include_graphics("https://splendoursofafricatours.com/uploads/destination_detail/22/volcanoes-Park-Rwanda-Banner.jpg")
```

```{r}
source("analysisFunctions.R")
```

# Data Availability

## {data-width=200}

###

This panel displays the availability of GNSS data for each Gorilla group.

##

# Statistics {data-navmenu="Home Range"}

## {data-width=200}

###

The collected GNSS data is used to calculate the home range of the Mountain Gorillas using Kernel density estimator with bivariate normal kernel using 95% of estimated distribution. Key statistics by Gorilla group are shown in this panel.

```{r, echo = F}
csv <- "Gorilla_ranging_Jan_May_21.csv"
df <- GPS2df(csv)

total_home_range <- round(ttlHR(df),1)
areadf <- gpHR(df)

max_area <- round(max(areadf$area),1)
min_area <- round(min(areadf$area),1)
mean_area <- round(mean(areadf$area),1)
```

## {data-width=300}

### Max Size (km²)
```{r}
valueBox(max_area, icon="ion-stats-bars", color="#5f8111")
```

### Min Size (km²)
```{r}
valueBox(min_area, icon="ion-stats-bars", color="#a3ca61")
```

### Mean Size (km²)
```{r}
valueBox(mean_area, icon="ion-android-home", color="#5f8111")
```

### Total Coverage (km²)
```{r}
valueBox(total_home_range, icon="ion-android-locate", color="#a3ca61")
```

## {data-width=500}

### Home Range Size Data Table

```{r functions}
nice_datatable <- function(df, caption = NULL, ...) {
  df %>%
    datatable(
      extensions = c("Buttons", "FixedHeader", "Scroller"),
      filter = "top",
      rownames = FALSE,
      escape = FALSE,
      caption = caption,
      options =
        list(
          dom = "Brtip",
          deferRender = FALSE,
          scrollY = 300,
          scroller = TRUE,
          scrollX = TRUE,
          fixedHeader = FALSE,
          buttons = c("copy", "csv"),
          pageLength = nrow(df),
          lengthChange = FALSE,
          ...
        )
    )
}
```

```{r}
nice_datatable(areadf %>% mutate_if(is.numeric, round, 2))
```

# Map {data-navmenu="Home Range"}

## {data-width=600}

###
```{r}
#Interactive Map

map <- mapDisplay(df)
map@map %>% setView(29.49, -1.5, zoom = 13)
```

## {data-width=400}

###
```{r}
#Overview Map

knitr::include_graphics("https://www.berggorilla.org/fileadmin/_processed_/4/e/csm_volcano_national_park_f577bb5a63.png")
```

###
```{r}
#Bar Chart: Home Range Size

fig1 <- areaBar(areadf)
ggplotly(fig1, tooltip=c("x","y")) %>% config(displayModeBar = FALSE)

```

# Group Dynamics

## {data-width=200}

This panel display information about how home range of different Mountain Gorilla groups overlap with each other. It includes a heatmap showing the interactions among groups, as well as a scatter plot to illustrate the relationship between home range size and total share of home range.

## {data-width=500}

###
```{r}
fig2 <- overlapBar(df)
ggplotly(fig2, tooltip=c("y")) %>% config(displayModeBar = FALSE)
```

###

```{r}
fig3 <- dynamicViz(df)
ggplotly(fig3) %>% config(displayModeBar = FALSE)
```

## {data-width=300} {.tabset }

### Interactions

```{r}
# Tab 2
#fig4 <- GPheatmap(df)
#fig4
invisible(GPheatmap(df))

```

### Data Table

```{r}
# Tab 1

gp_dyn_df <- overlapAnalysis(df)
names(gp_dyn_df) <- c("Primary Group","Home Range Size of Primary Group","Interacting Group","Home Range Overlapped Area","Percentage to Size of Primary Group")

nice_datatable(gp_dyn_df %>% mutate_if(is.numeric, round, 2))
```

# Walking Distance

### 

```{r}

```

# References

###



Calenge C (2006). “The package adehabitat for the R software: tool for the analysis of space and habitat use by animals.” Ecological Modelling, 197, 1035.

Tétreault, M., Franke, A. (2017). Home range estimation: examples of estimator effects. Applied raptor ecology: essentials from Gyrfalcon research. The Peregrine Fund, Boise, Idaho, USA, 207-242.

Walter,D., Fischer,J.W.,(2016). Home Range Estimation. In Manual of Applied Spatial Ecology (pp. 72–105). essay, Walter Applied Spatial Ecology Laboratory.